def score(p, subjects):
"""Scoring function"""
for subj in subjects:
print(' Running subject %s... ' % subj, end='')
# Figure out what our filenames should be
out_dir = op.join(p.work_dir, subj, p.list_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
for run_name in p.run_names:
fname = op.join(p.work_dir, subj, p.raw_dir,
(run_name % subj) + p.raw_fif_tag)
events, presses = extract_expyfun_events(fname)[:2]
for ii in range(len(events)):
events[ii, 2] = _expyfun_dict[events[ii, 2]]
fname_out = op.join(out_dir,
'ALL_' + (run_name % subj) + '-eve.lst')
mne.write_events(fname_out, events)
# get subject performance
devs = (events[:, 2] % 2 == 1)
has_presses = np.array([len(pr) > 0 for pr in presses], bool)
n_devs = np.sum(devs)
hits = np.sum(has_presses[devs])
fas = np.sum(has_presses[~devs])
misses = n_devs - hits
crs = (len(devs) - n_devs) - fas
print('HMFC: %s, %s, %s, %s' % (hits, misses, fas, crs))
def reconstruct_events(p, subjects):
"""Reconstruct events from expyfun tab file """
for subj in subjects:
print(' Running subject %s... ' % subj, end='')
# Figure out what our filenames should be
out_dir = op.join(p.work_dir, subj, p.list_dir)
for run_name in p.run_names:
print(subj)
fname = op.join(p.work_dir, subj, p.raw_dir,
(run_name % subj) + p.raw_fif_tag)
tab_file = op.join(p.work_dir, subj, p.list_dir,
(run_name % subj + '.tab'))
evs, _ = extract_expyfun_events(fname)[:2]
raw = mne.io.read_raw_fif(fname, allow_maxshield='yes')
data = read_tab(tab_file)
new_evs = np.zeros(evs.shape, dtype=np.int)
for i in range(len(evs)):
new_evs[i, 0] = raw.time_as_index(data[i]['play'][0][1])
# classify event type based on expyfun stimulus
new_evs[i, 1] = 0
if data[i]['trial_id'][0][0] == 'Dp01bw6-rms':
trigger = 103
elif data[i]['trial_id'][0][0] == 'Dp01bw1-rms':
trigger = 104
elif data[i]['trial_id'][0][0] == 'Dp01bw10-rms':
trigger = 105
new_evs[i, 2] = trigger
fname_out = op.join(out_dir,
'ALL_' + (run_name % subj) + '-eve.lst')
mne.write_events(fname_out, new_evs)
def score(p, subjects):
"""Scoring function"""
for subj in subjects:
print(" Running subject %s... " % subj, end="")
# Figure out what our filenames should be
out_dir = op.join(p.work_dir, subj, p.list_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
for run_name in p.run_names:
fname = op.join(p.work_dir, subj, p.raw_dir, (run_name % subj) + p.raw_fif_tag)
events, presses = extract_expyfun_events(fname)[:2]
for ii in range(len(events)):
events[ii, 2] = _expyfun_dict[events[ii, 2]]
fname_out = op.join(out_dir, "ALL_" + (run_name % subj) + "-eve.lst")
mne.write_events(fname_out, events)
# get subject performance
devs = events[:, 2] % 2 == 1
has_presses = np.array([len(pr) > 0 for pr in presses], bool)
n_devs = np.sum(devs)
hits = np.sum(has_presses[devs])
fas = np.sum(has_presses[~devs])
misses = n_devs - hits
crs = (len(devs) - n_devs) - fas
print("HMFC: %s, %s, %s, %s" % (hits, misses, fas, crs))
def score(file_path):
"""Scoring function"""
events, presses = extract_expyfun_events(file_path)[:2]
i = np.arange(len(events))
events[i, 2] -= 1
mask = events[i, 2] > 0
events = events[mask]
presses = np.array(presses)[mask].tolist()
# get subject performance
# boolean mask using modulus of target event and 2
targets = events[:, 2] % 2 == 0
has_presses = np.array([len(pr) > 0 for pr in presses], bool)
n_targets = np.sum(targets)
hits = np.sum(has_presses[targets])
false_alarms = np.sum(has_presses[~targets])
misses = n_targets - hits
print("HMF: %s, %s, %s" % (hits, misses, false_alarms))
return events
def score(p, subjects):
"""Scoring function"""
for subj in subjects:
print(' Running subject %s... ' % subj, end='')
# Figure out what our filenames should be
out_dir = op.join(p.work_dir, subj, p.list_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
for run_name in p.run_names:
print(subj)
fname = op.join(p.work_dir, subj, p.raw_dir,
(run_name % subj) + p.raw_fif_tag)
events, _ = extract_expyfun_events(fname)[:2]
events[:, 2] += 100
fname_out = op.join(out_dir,
'ALL_' + (run_name % subj) + '-eve.lst')
mne.write_events(fname_out, events)
def score(p, subjects):
"""Scoring function"""
for subj in subjects:
print(' Running subject %s... ' % subj, end='')
# Figure out what our filenames should be
out_dir = op.join(p.work_dir, subj, p.list_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
for run_name in p.run_names:
print(subj)
fname = op.join(p.work_dir, subj, p.raw_dir,
(run_name % subj) + p.raw_fif_tag)
# events = mne.find_events(mne.io.Raw(fname, allow_maxshield='True'), stim_channel='STI101')
#events = events[events[:, 2] == 1]
events, _ = extract_expyfun_events(fname)[:2]
events[:, 2] += 10
fname_out = op.join(out_dir,
'ALL_' + (run_name % subj) + '-eve.lst')
mne.write_events(fname_out, events)
#list_fname = '/Volumes/TimeMachineBackups/MEG_Data/SASI/sentnew2b_FishNew.lst' ======= list_fname = '/Volumes/TimeMachineBackups/MEG_Data/SASI/sentnew2a_FishNew.lst' >>>>>>> dcf4d606580048b88be0ee4055e6e022c5ef1d66 list_info = parse_list(list_fname) list_info_temp = list_info <<<<<<< HEAD raw_fname = '/Volumes/TimeMachineBackups/MEG_Data/SASI/sasi_129/sasi_129_raw_tsss_mc.fif' ======= raw_fname = '/Volumes/TimeMachineBackups/MEG_Data/SASI/sasi_101/sasi_101_raw_tsss_mc.fif' >>>>>>> dcf4d606580048b88be0ee4055e6e022c5ef1d66 sentnew2_events, _ = extract_expyfun_events(raw_fname)[:2] # Takes 20 seconds # Format ids sentnew2_events[:, 2] += 10 sentnew2_events_offset = np.zeros([1500,3], dtype=int) sentnew2_critical = np.zeros([1500,3], dtype=int) <<<<<<< HEAD fname_out = '/Volumes/TimeMachineBackups/MEG_Data/SASI/sasi_129/sentnew2a_FishNew-eve.lst' ======= fname_out = '/Volumes/TimeMachineBackups/MEG_Data/SASI/sasi_101/sentnew2a_FishNew-eve.lst' >>>>>>> dcf4d606580048b88be0ee4055e6e022c5ef1d66 #op.join(out_dir, 'ALL_' + (raw_fname) + '-eve.lst') mne.write_events(fname_out, sentnew2_events) # NEXT : loop through list_info for each sentence & add offset to timestamp
def score(p, subjects):
"""Use expyfun to extract events write MNE events file to disk."""
for subj in subjects:
print(" Running subject %s... " % subj, end="")
# Figure out what our filenames should be
out_dir = op.join(p.work_dir, subj, p.list_dir)
if not op.isdir(out_dir):
os.mkdir(out_dir)
for run_name in p.run_names:
# Extract standard events
fname = op.join(p.work_dir, subj, p.raw_dir,
(run_name % subj) + p.raw_fif_tag)
fname_out = op.join(out_dir, f"ALL_{run_name % subj}-eve.lst")
events, _ = extract_expyfun_events(fname)[:2]
events[:, 2] += offsets[run_name.split('_')[-1]]
if run_name in ("%s_am", "%s_ids"):
mne.write_events(fname_out, events)
continue
# Find the right mismatch .tab file
raw = mne.io.read_raw_fif(fname, allow_maxshield="yes")
exp_subj = subj.split("_")[1].rstrip("ab")
tab_files = sorted(glob.glob(op.join(tabdir, f"{exp_subj}_*.tab")))
assert len(tab_files)
good = np.zeros(len(tab_files), bool)
got = list()
ts = list()
for tab_file in tab_files:
with open(tab_file, "r") as fid:
header = fid.readline().lstrip("#").strip()
if "runfile" in header:
# errant session that breaks things
assert subj == "bad_130a"
header = re.sub('"runfile.*\'\\)"', "'1'", header)
header = json.loads(header.replace("'", '"'))
assert header["participant"] == exp_subj
if "." in header["date"]:
fmt = "%Y-%m-%d %H_%M_%S.%f"
else:
fmt = "%Y-%m-%d %H_%M_%S"
t_tab = datetime.datetime.strptime(
header["date"], fmt).replace(tzinfo=timezone("US/Pacific"))
t_raw = raw.info["meas_date"]
ts.append(t_tab)
# offsets between the Neuromag DAQ and expyfun computer
off_minutes = abs((t_raw - t_tab).total_seconds() / 60.0)
got.append(
(off_minutes, header["exp_name"], header["session"]))
# pick something in the right time frame, and the correct
# session
good = [
m < 120 and e == "syllable" and s in ("1", "3")
for m, e, s in got
]
if sum(good) == 2:
idx = np.where(good)[0]
sizes = [os.stat(tab_files[ii]).st_size for ii in idx]
print(f" Triaging based on file sizes: {sizes}")
for ii in idx:
good[ii] = False
good[idx[np.argmax(sizes)]] = True
assert sum(good) == 1, sum(good)
idx = np.where(good)[0][0]
fname_tab = tab_files[idx]
print(
f' Selected {tab_files[idx]}:\n'
f' Raw data {raw.info["meas_date"].astimezone(ts[idx].tzinfo)}\n'
f' Tab file {ts[idx]}')
# We should only have one candidate file
assert sum(good) == 1, sum(good)
fname_tab = tab_files[np.where(good)[0][0]]
data = read_tab(fname_tab, allow_last_missing=True)
# Correct the triggers
if subj in ("bad_921a", "bad_925a"):
use_map = OTHER_TRIGGER_MAP
else:
use_map = TRIGGER_MAP
new_nums = np.array([use_map[d["trial_id"][0][0]] for d in data],
int)
exp_times = [d["play"][0][1] for d in data]
# Sometimes we are missing the last one
assert len(data) >= len(events), (len(data), len(events))
n_missed = len(data) - len(events)
if n_missed:
if subj == "bad_117a":
sl = slice(n_missed - 1, -1, None)
else:
sl = slice(None, -n_missed, None)
data = data[sl]
new_nums = new_nums[sl]
exp_times = exp_times[sl]
corr = np.corrcoef(events[:, 0], exp_times)[0, 1]
assert corr > 9e-20, corr
wrong = new_nums != events[:, 2]
if wrong.any():
print(f" Replacing {wrong.sum()}/{len(wrong)} TTL IDs")
events[:, 2] = new_nums
assert np.in1d(events[:, 2], IN_NUMBERS).all()
print(" Counts: " +
" ".join(f"{name.upper()}: {(events[:, 2] == num).sum()}"
for name, num in zip(IN_NAMES, IN_NUMBERS)))
mne.write_events(fname_out, events)
if list_type == 'B':
list_fname = data_path + 'sentnew2b_FishNew.lst'
subjs = [
'sasi_110', 'sasi_114', 'sasi_118', 'sasi_120', 'sasi_130', 'sasi_134',
'sasi_141', 'sasi_144', 'sasi_147'
]
else:
print("list_type must be A or B")
list_info = parse_list(list_fname)
list_info_temp = list_info
for i in subjs:
raw_fname = op.join(data_path, '%s' % i, 'raw_fif', '%s_raw.fif' % i)
sentnew2_events, _r = extract_expyfun_events(raw_fname)[:2]
# Format ids
sentnew2_events[:, 2] += 10
sentnew2_events_offset = np.zeros([1500, 3], dtype=int)
sentnew2_critical = np.zeros([1500, 3], dtype=int)
fname_out = op.join(data_path, '%s' % i, 'lists',
'orig_events_%s-eve.lst' % i)
mne.write_events(fname_out, sentnew2_events)
# Loop through list_info for each sentence & add offset to timestamp
num_sentences = (len(sentnew2_events)) - 1
# Loop through sentences
total_word_count = 0
last_word = 0
#for sentence_count in range(0, 3):
